The wearable autonomous positioning system composed by array inertial devices can significantly improve the positioning accuracy of the wearer, but the array inertial devices in the wearable autonomous positioning system are difficult to avoid failure in the process of operation. To address the phenomenon of array accelerometer noise increasing fault in the autonomous positioning system worn by emergency rescue personnel, a Convolutional Neural Networks (CNN) based array accelerometer fault detection method is proposed, using the Generalized Likelihood Ratio (GLR) test to compare the array gyroscope with the array accelerometer, The GLR test is used to compare the array gyroscope control data, and then the CNN calculates the mapping result between the accelerometer data and the gyroscope control data to achieve fast detection of the array accelerometer growth increase fault. Through the twelve IMU array data fusion and fault detection test results show that the detection method can quickly and effectively detect the typical fault of accelerometer noise increase in the array inertial device, the fault detection rate ≥ 98%, the effect is obvious.